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This paper studies the hierarchical hub location problem with single assignment for a three-level hub network and focus on several solution methods. The top level consists of a network of central hubs, directly connected with each other. The second and third level are star networks connecting each hub to exactly one central hub and connecting each node to exactly one hub or central hub. The objective is to decide on the location of the central hubs and the hubs, the allocation of hubs to central hubs and the allocation of nodes to hubs or central hubs, while minimizing the total distribution cost. Therefore, a linear mixed integer program formulation is used. Next, two models are proposed to select good values for the input parameters of the MIP formulation. The total solving time required for the full problem appears to be more than 14 hours for one set of number of central hubs and number of hubs to be opened. Therefore, this paper discusses three approximate heuristic solution methods, to find a nearly optimal solution in a much more acceptable time interval. Eventually, the performance of these heuristic solution methods is compared with the optimal solution of the full-sized problem.
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De aandelenbeurs spreekt vaak tot de verbeelding. De financiële analisten worden door leken aanzien als personen in strak maatpak die aan de hand van verkooppraatjes zo veel mogelijk commissie proberen te verdienen. Deze paper biedt een algemene inkijk in de wereld van aandelen en analisten en gaat na of eerdergenoemde statements wel kloppen. Na een algemene inkijk over de aandeelprijzen, hoe deze worden gewaardeerd en wat mogelijke invloeden hierop kunnen zijn, wordt ook de wereld van de financiële analisten toegelicht. Wie ze zijn, wat ze doen en of de analist wel een accuraat hulpmiddel is voor de belegger. Ten slotte worden er vier mogelijke invloeden door de financieel analist besproken. Dit gaat over de invloed op aandeelprijzen van de analisten. Welke invloed ze hebben op de algemene beurshandel. Ook de gevolgen van verschillende opinies van analisten wordt bekeken. En uiteindelijk wordt beschreven wat de mogelijke gevolgen van analisten op bedrijfsfraude kan zijn.
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In deze masterthesis zijn twee data analyse technieken toegepast op parkeergegevens van de stad Alkmaar (NL) om patronen hierin te herkennen. De eerste techniek is datamining, waar een globaal beeld gegeven wordt over de data. Om deze techniek toe te passen is er gebruik gemaakt van de software Disco. Dit is een een open source tool waarmee patronen herkenbaar gemaakt kunnen worden. De tweede techniek die toegepast is, is Formal Concept Analysis (FCA) hiermee kunnen concretere patronen gevonden worden in een dataset. De software die hiervoor gebruikt wordt is Concept Explorer, ook deze tool is vrij te verkrijgen via het internet. In deze thesis worden enkel patronen gezocht in de gegevens van de bewoners omdat enkel deze van elkaar onderscheiden kunnen worden. Dit is de eerste keer dat er met deze analyse tools op parkeerdata gekeken wordt. Dit moet in het achterhoofd gehouden worden tijdens het lezen van deze tekst.
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This work gives a broad understanding in how certain machine learning algorithms work and behave in the real world. The classification of the algorithm gives a structural representation of its functionality and risk for biases. The biases itself could be directly linked to the algorithm but also been implied by the data or even been introduced by loops in the system. These biases can lead ethical concerns, such as privacy. To accommodate this a complex legal structure is introduced, one of the most recent and important being the GDPR-law which is used in the European Union, on top of the local laws. To demonstrate the importance and real world application, the Schufa organisation is described. This credit scoring firm uses a black box which led to critique and ethical problems. Secondly the possibilities of recent algorithms, such as the support vector machine (SVM) is taken under the loop. This has led to a better credit assessment, and with future improvements, it should be possible to eliminate the current dilemmas. Lastly the wide applications of machine learning algorithm is noted by the example of Word2Vec. This is to indicate possible ethical dilemmas, such as gender biases. And more importantly how to eliminate them.
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This thesis issues the planning and assignment of incoming flights and their inbound baggage to baggage conveyor belts in the baggage claim area of an airport. Inbound baggage handling represents a crucial process in the activities ongoing in the airport terminal. This directly affects the airport performance and the quality of service perceived by the passengers. Using a set of instances based on real data from Brussels Airport, a robust planning model is constructed that takes airline preferences, potential overlap and passenger satisfaction into account. It ensures minimal overlap whilst using existing infrastructure as efficiently as possible. First, a mathematical model of the problem is formulated. Its optimal solution serves as a benchmark for the implementation of two metaheuristics, namely Simulated Annealing and Tabu Search. The realistic data is featured in a comparative analysis between the general optimal solution of the mathematical model and both metaheuristics. The metaheuristics prove to be useful by delivering high-quality solutions in limited computational runtime. The results show that both Simulated Annealing and Tabu Search are capable of achieving a relative performance in the range of 95.66% up to 99.25%, compared to the optimal flight to baggage carousel assignment. The algorithms account for a planning horizon of three hours with a limited runtime of three minutes. Ultimately, metaheuristics can relieve dispatchers from daily planning operations in airports, making it more efficient.
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The main reason behind the current climate change is global warming. Human influence on the climate system is clear, and recent anthropogenic emissions of greenhouse gases are the highest in history. The current atmospheric CO2 concentration reach higher than 400 ppm, higher than the largest peaks of 300 ppm in the past 425,000 years. If current emission trends continue, the global average surface temperature will have warmed 1.2 to 1.7°C by 2050, compared to pre-industrial times. The largest source of greenhouse gases is still the burning of fossil fuels. In order to not exceed the 2°C warming limits and emissions goals set during the recent climate conference in Paris, the use of fossil fuels as an energy source should be replaced by renewable sources. To meet the ambitious goals of the European Union, an emission reduction pathway till 2050 is constructed, together with the yearly required investment. This yearly investment should increase from 75 billion euro in 2016, to peak around 2035 at about 245 billion euro, before decreasing again to about 75 billion euro in 2050.
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The periodic vehicle routing problem is a well-studied problem in which routes for capacitated vehicles are planned over a planning horizon of several periods and customers have a set of allowed visit schedules. The periodic vehicle routing problem with driver consistency (PVRP-DC) is a new version of this problem in which each customer should always be visited by the same driver. In this thesis we illustrate the use and need of solutions to this problem in the industry through interviews with three different companies. We then go on to implement three simple meta-heuristics to solve the PVRP-DC: a Tabu-search, a Simulated Annealing search and a Genetic Algorithm. We report computational results on a wide range of instances and compare these to the integer linear programming solutions from [1].
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Batteries have become an indispensable part of our modern lives. Yet almost invisible to us, they are a remarkable invention with a long and storied past as well as an equally promising future. The aim of this thesis is to provide a comprehensive overview of the advancements and applications in battery technology. This has been achieved by first giving some statistical research of the battery market, analyzing batteries on a technical level, evaluating the waste management and end-of-life treatments and investigating the regulation and legislation. In the second part of the thesis, three special topics associated with battery technology are defined, described and disclosed.
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This work gives a broad understanding in how certain machine learning algorithms work and behave in the real world. The classification of the algorithm gives a structural representation of its functionality and risk for biases. The biases itself could be directly linked to the algorithm but also been implied by the data or even been introduced by loops in the system. These biases can lead ethical concerns, such as privacy. To accommodate this a complex legal structure is introduced, one of the most recent and important being the GDPR-law which is used in the European Union, on top of the local laws. To demonstrate the importance and real world application, the Schufa organisation is described. This credit scoring firm uses a black box which led to critique and ethical problems. Secondly the possibilities of recent algorithms, such as the support vector machine (SVM) is taken under the loop. This has led to a better credit assessment, and with future improvements, it should be possible to eliminate the current dilemmas. Lastly the wide applications of machine learning algorithm is noted by the example of Word2Vec. This is to indicate possible ethical dilemmas, such as gender biases. And more importantly how to eliminate them.
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In the past, machine scheduling posed an insignificant problem to the production environment because of its limited impact on the costs of a business. Due to the growing costs of materials, labour, energy and transportation the use of efficient job schedules become increasingly more important. One ground-breaking production environment is additive manufacturing (AM). The AM production environment is, when nesting is applied, inherently efficient in the use of materials and printing volume. Production can be a slow process and thus efficiently scheduling printing jobs has a great impact on the total tardiness of jobs which allows the possible avoidance of tardiness costs. In this thesis, two meta-heuristics produce improved schedules for a theoretical translation of the AM production environment. Simulated annealing (SA) and genetic algorithm (GA), a local search and a population-based search technique respectively, solve an unrelated parallel machine scheduling problem with eligibility constraints for the minimisation of total tardiness. The performance of the proposed algorithms is evaluated by several different measures. The algorithms use realistic data sets to better represent the actual AM production environment. This thesis concludes that SA proves to be a faster scheduling method than GA while GA outperforms SA in terms of the achieved minimisation of total tardiness.
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